Oracle Blog

For Oracle Partners in EMEA

Friday Feb 20, 2015

There has been a lot
of excitement about Oracle Big Data Discovery, and it is now
generally available to customers and partners and can be sold and installed
now.

This is a stunningly
visual, intuitive tool that enables you to leverage the power of Hadoop and
turn raw data into business insight in minutes — without learning complex
products or relying only on specialists.

From a partner
perspective it is perfect for proof of concept work with your clients to reveal
the value they can find in their large information reservoirs combining a variety
of data and text sources; potentially leading to much more and deeper analysis
as part of an overall big data information architecture.

Thursday Apr 25, 2013

For those of you just getting interested in “Advanced Analytics”, you
may still be wondering “What is R ?”…R is an open-source
language and environment for statistical computing and data visualization: and
it works with OBI to enrich the graphics and predictive capabilities: see here a quick preview on YouTube.

It is being taught in
colleges and universities in courses on statistics and advanced analytics – often
in preference to more traditional statistical software tools – and so skills in
R are readily available among
younger graduates.

For you experts in the field who use R anyway, the key question is why use “Oracle’s version” ?

The
tight integration between R, Oracle Database 11g, and Hadoop enables R users to
write one R script that can run in three different environments: a laptop
running open source R, Hadoop running with Oracle Big Data Connectors, and Oracle Database 11g.

For
large analyses on large Oracle data-sets, it is much faster and easier to do
this “inside” the database, than exporting the data into another specialised
external data format. Some
of the benchmarks below, show 4x ~ 20x + faster for various operations, and
even 100x + for some data scoring algorithms.

For Oracle Advanced Analytics Option, Oracle R Enterprise in-DB
functionality, the performance gains come through the R-to-SQL transparency layer
for native SQL performance and the OAA/ORE “mapping” to the OAA/Oracle Data
Mining SQL based hi-performance data mining algorithms and statistical
functions that are native in-DB parallelized implementations of the
algorithms.

Also, besides the simpler and more scalable architecture, the majority
of the performance gains stem from eliminating the extract, mine, apply models,
import outer loop which can take weeks to months, largely due to “human time”
sinks to manually translate the data transformations and model logic to native
SQL for in-DB deployment. That conversion process is tedious, time
consuming and error prone. The OAA in-DB performance therefore cuts that
latency time down to secs / mins / hours.